Optimal scheduling model of microgrid based on improved dung beetle optimization algorithm

In view of the strong uncertainty and intermittency of distributed power sources in microgrids and the shortcomings of the traditional dung beetle optimizer (DBO) algorithm with slow convergence, poor robustness and ease of falling into a local optimum, an optimal scheduling model for microgrids based on the improved dung beetle optimization algorithm is proposed. First, a multiobjective optimal scheduling model of the microgrid is constructed and a typical daily output scenario generation method for wind power generation and photovoltaic power generation is constructed based on the Gaussian kernel density estimation, Frank-copula and K-means clustering algorithms. Second, to address the shortcomings of the DBO algorithm, the spiral position update strategy, adaptive weight factor, levy flight strategy and t-distribution variation strategy are introduced on the basis of the DBO algorithm, which effectively solves the problem of premature convergence of particles owing to falling into a local optimum. Finally, five benchmark test functions were selected for simulation experiments. Finally, the simulation results show that the computational performance of the IDBO algorithm is significantly better than the other five intelligent algorithms. The algorithm proposed in this paper also achieves more satisfactory results in microgrid optimal scheduling based on the scenario generation method.